A Methodology to Implement Box-Cox Transformation When No Covariate is Available

Dag O., Asar O., İLK DAĞ Ö.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.43, sa.7, ss.1740-1759, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43 Sayı: 7
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1080/03610918.2012.744042
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1740-1759
  • Anahtar Kelimeler: Data transformation, Maximum likelihood estimation, Non-informative covariate, Normality, Regression analysis, Statistical distributions, REGRESSION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Box-Cox transformation is one of the most commonly used methodologies when data do not follow normal distribution. However, its use is restricted since it usually requires the availability of covariates. In this article, the use of a non-informative auxiliary variable is proposed for the implementation of Box-Cox transformation. Simulation studies are conducted to illustrate that the proposed approach is successful in attaining normality under different sample sizes and most of the distributions and in estimating transformation parameter for different sample sizes and mean-variance combinations. Methodology is illustrated on two real-life datasets.